Evidence map›Paper›PMID 38800159›Full record

ArticleCureus2024

The Potential of ChatGPT for High-Quality Information in Patient Education for Sports Surgery.

Ali Yüce, Nazım Erkurt, Mustafa Yerli, Abdulhamit Misir

Abstract read
In one paragraph

Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Expert nurses vs. ChatGPT for postoperative lumbar disk nursing care.Proceedings (Baylor University. Medical Center) · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Ali YüceDepartment of Orthopedics and Traumatology, Prof. Dr. Cemil Taşçıoğlu City Hospital, Istanbul, TUR.
Nazım ErkurtDepartment of Orthopedics and Traumatology, Prof. Dr. Cemil Taşçıoğlu City Hospital, Istanbul, TUR.
Mustafa YerliDepartment of Orthopedics and Traumatology, Prof. Dr. Cemil Taşçıoğlu City Hospital, Istanbul, TUR.
Abdulhamit MisirDepartment of Orthopedics and Traumatology, Bahcesehir University Göztepe Medicalpark Hospital, Istanbul, TUR.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectiveArtificial intelligence (AI) advancements continue to have a profound impact on modern society, driving significant innovation and development across various fields. We sought to appraise the reliability of the information offered by Chat Generative Pre-Trained Transformer (ChatGPT) regarding diseases commonly associated with sports surgery. We hypothesized that ChatGPT could offer high-quality information on sports-related diseases and be used in patient education.

methodsOn September 11, 2023, specific sports surgery-related diseases were identified to ask ChatGPT-4 (personal communication, March 4, 2023). The informative texts provided by ChatGPT were recorded by a non-observer senior orthopedic surgeon for this study. Ten texts provided by ChatGPT related to sports surgery diseases were evaluated blindly by two observers. Observers assessed and scored these texts based on the sports surgery-specific scoring (SSSS) and DISCERN criteria. The precision of the disease-related information offered by ChatGPT was evaluated.

resultsThe calculated average DISCERN score of the texts in the study was 44.75 points and the average SSSS score was 13.3 points. In the interclass correlation coefficient analysis of the measurements made by the observers, the agreement was found to be excellent (0.989; p < 0.001).

conclusionChatGPT has the potential to be used in patient education for sports surgery-related diseases. The potential to provide quality information in this regard seems to be an advantage.

Indexed as

artificial intelligence(ai)chatbotschatgptorthopedic sports medicinepatient educationsports surgery

Identifiers

PMID38800159
PMCPMC11116739

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.